Puttaraju Rangaswamy

#Techie | ↔️ |

[CodeCrafters @CodeCrafters11]

Python skills that actually get paid 💰

• 🐍 Python Basics → start here
• 📁 File Handling → automate boring work
• 🌐 APIs → connect real apps
• 🧠 OOP → write cleaner code
• 🗄️ SQL → work with data
• ⚡ FastAPI → build backends
• 📊 Pandas → analyze data
• 🤖 ML Basics → enter AI
• ☁️ AWS + Python → cloud automation
• 🧪 Testing → write reliable code

Python alone won’t get you hired.
Building real projects with Python is what creates opportunities.

[Tom Dörr @tom_doerr]

Serves a secure home server operating system as a web desktop across Linux, macOS, Windows, Docker, and Oracle Cloud.

https://t.co/EKmZiXj7AW

[AI_Explorer @ai_explorer25]

Learn AI for free directly from top companies.

1 - Anthropic:
https://t.co/cc4SxBnCHF

2 - Google:
https://t.co/r1bv5Xgfil

3 - Meta:
https://t.co/UALdoelTnj

4 - NVIDIA:
https://t.co/JKTgY3n14y

5 - Microsoft:
https://t.co/izcZcNQHDl

6 - OpenAI:
https://t.co/nY93DIwNTe

7 - IBM:
https://t.co/lIYoazaXfW

8 - AWS:
https://t.co/770qBjwHeW

9 DeepLearning. AI:
https://t.co/SnPZ288j8u

10 - Hugging Face:
https://t.co/AAym51zedH

Comment "Learning" if you find this helpful.

[ExamAdda @examaddaorg]

Fix your architectural debt with these 7 foundational system design principles:

1️⃣ DRY: Stop duplicating logic.
2️⃣ KISS: Stop over-engineering code.
3️⃣ YAGNI: Drop premature features.
4️⃣ Law of Demeter: Limit object knowledge.
5️⃣ Separation of Concerns: Isolate system responsibilities.
6️⃣ Coupling & Cohesion: Build independent components.
7️⃣ Object Composition: Assemble reusable parts.

Which One you're struggle with the most?

Learn More 👇
https://t.co/zmwhYlGFkr

#SystemDesign #Interview #Coding #Examadda

[Suryansh Tiwari @Suryanshti777]

Most people want to become AI Engineers

Very few know what to learn next

That's why I'm giving away one of the best Agentic AI Engineer Roadmaps for FREE to the first 4500 people only

Inside you'll learn

✅ Python Fundamentals
✅ LLM Fundamentals
✅ LangChain LangGraph CrewAI AutoGen
✅ LCEL Runnables & Workflows
✅ Memory Systems
✅ Tool Integrations
✅ RAG Systems
✅ Multi Agent Systems
✅ Real World AI Projects
✅ Interview Questions & Answers

72 Hours Only

How to get

Follow me (so I can DM you)
Like + RT
Comment "ROADMAP"

Once the limit is reached I'll stop sending it.

[Roan @RohOnChain]

Don't waste 2 years learning to build LLMs like Claude & ChatGPT.

Stanford just dropped a 2 hour course on how to build LLMs from scratch.

• 00:00 - LLM tokenization
• 25:44 - how LLMs decode user prompts
• 35:40 - training pipeline of LLMs
• 1:16:47 - LLM architecture from scratch

Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs.

Bookmark this & give 2 hours today, no matter what. Then read the article below.

[KOMAL👩‍💻 @KomalWCode]

Try to go through it 🤝

[Nikki Siapno @NikkiSiapno]

Docker vs Kubernetes

𝗗𝗼𝗰𝗸𝗲𝗿 is a containerization platform that solves the “it works on my machine” problem.

It builds images, runs containers, and primarily runs them on a single host. Applications are packaged with everything they need and run as lightweight, isolated containers. The focus is on running software consistently with simple primitives like images and containers.

𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 is a cluster orchestration system.

It manages containers across multiple machines using a control plane that schedules and maintains containerized workloads. It works with Pods and reconciles actual state to desired state. The focus is on operating software at scale with capabilities like self-healing, autoscaling, and rolling updates.

Kubernetes doesn’t create containers. It orchestrates them. Docker runs containers. Kubernetes keeps them running.

What else would you add?

➕ Follow me ( @NikkiSiapno ) to improve at system design.

[Shubh Jain @shubh19]

🚨40 most asked DSA questions to clear your next interview

> Arrays and Strings:
1. Find the maximum sum subarray.
2. Find all substrings that are palindromes.
3. Implement the "two sum" problem.
4. Implement Kadane's algorithm for maximum subarray sum.
5. Find the missing number in an array of integers.
6. Merge two sorted arrays into one sorted array.
7. Check if a string is a palindrome.
8. Find the first non-repeating character in a string.
9. Write a program to remove duplicates from a sorted array.

> Linked List:
10. Reverse a linked list.
11. Detect a cycle in a linked list.
12. Find the middle of a linked list.
13. Merge two sorted linked lists.
14. Implement a stack using linked list.
15. Find the intersection point of two linked lists.

> Stack & Queue:
16. Implement a stack using an array.
17. Implement a stack that supports push, pop, top, and retrieving the minimum element.
18. Implement a circular queue.
19. Design a max stack that supports push, pop, top, retrieve maximum element.
20. Design a queue using stacks.

> Trees and Binary Search Trees:
21. Find the height of a binary tree.
22. Find the lowest common ancestor of two nodes in a binary tree.
23. Validate if a binary tree is a valid binary search tree.
24. Serialize and deserialize a binary tree.
25. Implement an inorder traversal of a binary tree.
26. Find the diameter of a binary tree.
27. Convert a binary tree to its mirror tree.

> Graphs:
28. Implement depth-first search (DFS).
29. Implement breadth-first search (BFS).
30. Find the shortest path between two nodes in an unweighted graph.
31. Detect a cycle in an undirected graph using DFS.
32. Check if a graph is bipartite.
33. Find the number of connected components in an undirected graph.
34. Find bridges in a graph.

> Sorting & Searching:
35. Implement (bubble, insertion, selection, merge) sort.
36. Implement quicksort.
37. Implement binary search.
38. Implement interpolation search.
39. Find the kth smallest element in an array.
40. Given an array of integers, count the number of inversions it has. An inversion occurs when two elements in the array are out of order.

[Suraj Sharma @suraj_sharma14]

As an AI Engineer, you must build these projects.

Systems that prove you can ship production AI.

1.) Production RAG with Citations
PDF Q&A bot that cites page numbers, implements hybrid search, reranking, and grounding.
Stack: LangGraph + SQLite-vec + cross-encoder.

2.) Cost-Optimized Model Router
System that routes queries by complexity to cheap/expensive models, tracks spend per request. Stack: LiteLLM + Prometheus + custom router.

3.) Multi-Agent Research System
Supervisor coordinates researcher, writer, fact-checker with consensus logic and human approval gates.
Stack: CrewAI + audit trail.

4.) Automated Eval Harness
Runs 100+ golden test cases before every deploy, blocks regressions, tracks quality trends.
Stack: DeepEval + RAGAS + LangSmith.

5.) Real-Time Observability Dashboard
Distributed tracing, cost per request, latency percentiles, error rates, alerting on anomalies.
Stack: OpenTelemetry + Grafana + Prometheus.

6.) Security Guardrail Middleware
Detects prompt injection, redacts PII, filters outputs, enforces rate limits, sandboxed execution.
Stack: Guardrails AI + custom rules.

7.) Local-First Development Environment
Ollama + SQLite-vec + FastAPI, zero API costs, offline testing, mirrors production architecture.
Stack: Ollama + LanceDB + Docker.

8.) Streaming Copilot UI
Real-time token streaming, optimistic UI updates, graceful degradation on latency, error recovery.
Stack: Next.js + Vercel AI SDK.

9.) Fine-Tuning Pipeline with LoRA
Dataset preparation, instruction tuning, DPO alignment, evaluation before/after, catastrophic forgetting prevention.
Stack: PEFT + Hugging Face.

10.) Multi-Tenant SaaS Agent
User isolation, per-tenant rate limiting, usage-based billing, data partitioning, audit trails.
Stack: Supabase + Stripe + LangGraph.

11.) CI/CD for AI Systems
GitHub Actions, automated testing, canary deployments, automatic rollback on quality drop, feature flags.
Stack: GitHub Actions + ArgoCD.

12.) Vector Database at Scale
Hybrid search, metadata filtering, embedding caching, index optimization, backup/recovery procedures.
Stack: Qdrant or Weaviate.

13.) Agent Memory System
Short-term buffers, long-term recall, context compression, cross-session sync, memory eviction policies.
Stack: Redis + vector DB.

14.) Production Inference Server
vLLM or SGLang deployment, KV cache optimization, continuous batching, quantization, load balancing. Stack: vLLM + Kubernetes.

15.) Human-in-the-Loop Workflow
Uncertainty detection, approval UI, pause/resume logic, validated context, full audit trail.
Stack: LangGraph + custom UI.

16.) Agentic Automation Pipeline
Webhooks, async execution, idempotency, dead-letter handling, retry logic with exponential backoff.
Stack: FastAPI + Celery.

17.) Domain-Specific Benchmark
Create eval suite for legal, medical, finance, or code. Public leaderboard, community adoption.
Stack: Custom + pytest.

18.) Open Source Contribution
Extend LangGraph, CrewAI, or LlamaIndex. Fix bug, add feature, write docs, publish benchmarks.
Stack: Your choice.

happy building...

Bookmark & Repost!

[Suraj Sharma @suraj_sharma14]

If I had 6 months to become an Agentic AI Engineer.

I would do this.

Stage 1: Python and Async Foundations
asyncio, FastAPI, event-driven architecture, error handling, API integration patterns.

Stage 2: LLM Fundamentals for Agents
Context management, model routing, token economics, latency tradeoffs, failure modes.

Stage 3: Tool Calling and Structured Outputs
Pydantic validation, function calling schemas, error recovery, dynamic tool discovery.

Stage 4: Memory and State Management
Short-term buffers, long-term vector recall, context compression, cross-session sync.

Stage 5: Single Agent Workflows
ReAct loops, plan-and-execute, self-reflection, iteration limits, graceful degradation.

Stage 6: Multi-Agent Orchestration
LangGraph or CrewAI, supervisor patterns, message passing, conflict resolution, handoffs.

Stage 7: Human-in-the-Loop Systems
Uncertainty detection, approval gates, audit trails, resume logic, intervention points.

Stage 8: Evaluation and Quality Assurance
Automated eval harnesses, LLM-as-a-judge, regression testing, hallucination metrics.

Stage 9: Observability and Tracing
Distributed tracing, cost dashboards, latency monitoring, alerting, debug pipelines.

Stage 10: Security and Guardrails
Prompt injection defense, output filtering, PII redaction, sandboxed execution, compliance.

Stage 11: Production Deployment
vLLM or SGLang, Kubernetes scaling, CI/CD for agents, canary releases, rollback strategies.

Stage 12: Open Source and Portfolio
Ship autonomous agents publicly, write architecture docs, record demos, contribute to libs.

Most people stay stuck watching tutorials.

Builders get hired.

Bookmark it.

[Uday👨‍💻 @uday_devops]

📊 Data Engineer

Stage 1: Python
Stage 2: SQL
Stage 3: Git + GitHub
Stage 4: Linux
Stage 5: Data Modeling
Stage 6: ETL / ELT
Stage 7: Apache Spark
Stage 8: Airflow
Stage 9: Kafka
Stage 10: Data Warehouses (Snowflake / BigQuery)
Stage 11: Cloud Data Pipelines
Stage 12: Build Scalable Data Platforms

🏆 Data Engineer unlocked 🙌

[diva @divaagurlxw]

Want to become a backend developer? learn these concepts

-System Design (scalability, microservices)
-APIs (REST, GraphQL, gRPC)
-Database Systems (SQL, NoSQL)
-Distributed Systems (consistency, replication)
-Caching (Redis, Memcached)
-Security (OAuth2, JWT, encryption)
-DevOps (CI/CD, Docker, Kubernetes)
-Performance Optimization (profiling, load balancing)
-Cloud Services (AWS, GCP, Azure)
-Monitoring (Prometheus, Grafana)

Pick up a language..
Stop jumping from one language to the other

[Ritika Agrawal @RitikaAgrawal08]

What is RAG ? ✨

RAG stands for Retrieval-Augmented Generation.

It's a technique that allows an AI application to retrieve relevant information before asking the LLM to generate a response.

Instead of relying only on what the LLM learned during training, the application first looks up information from external sources, then includes it in the prompt.

How RAG works? ✨
1. You ask a question.
2. The application searches a knowledge source.

It might retrieve :
→ Company documentation
→ PDFs
→ Wikis
→ Database records
→ Knowledge bases
→ Vector databases

The retrieved information is added to the prompt as additional context.

The LLM generates a response using that information.

Isn't that just context? ✨
Not exactly.

Context is everything the LLM receives before generating a response.

RAG is one technique the application uses to retrieve relevant information and turn it into context.

The LLM doesn't know whether that information came from you or was retrieved automatically.

It simply receives the final context and generates the response.

A simple way to remember it :

📝 Context = Everything the LLM sees.
📚 RAG = One way the application builds that context.

As AI applications continue to evolve, RAG has become one of the core building blocks for working with company knowledge, documentation, and constantly changing data.

That's why you'll find it in many modern AI products, including ChatGPT, Claude Code, Cursor, and Bug0.

_____________________

Thanks for reading till the end!🙌
In tomorrow's post, I will explore more about RAG.

[• nanou • @NanouuSymeon]

How to Get a Job as an AI Developer in 6 Months:

🚀 Month 1
🧠 Learn Python basics
📊 Understand math essentials
📚 Learn how models work at a high level

⚙️ Month 2
📘 Learn NumPy, Pandas, Matplotlib
🤖 Start with Machine Learning
🔍 Build 3 ML mini-projects

🛠️ Month 3
🧠 Learn PyTorch or TensorFlow
📸 Build a small neural network
🎧 Try 1 NLP project + 1 Computer Vision project
🧪 Understand losses, activations, overfitting

🧠 Month 4
⚡ Learn LLM basics
🛠️ Use OpenAI/Anthropic
🔗 Build RAG apps, chatbots, agents, and tools
🧩 Learn vector databases

🌍 Month 5
🌐 Launch 3 real projects
🚀 Deploy on Vercel, Render, or AWS
⚡ Create a polished GitHub + portfolio

💼 Month 6
📄 Build an AI-specific resume
💬 Join AI dev communities & hackathons
📨 Apply to 2–5 AI/ML roles daily
🧠 Prep for interviews

[Tech Fusionist @techyoutbe]

95% of Kubernetes Interviews Ask These 30 Concepts.
Are You Ready?

[Smart👨‍💻 | Software Engineer @smartnakamoura]

15 backend concepts you should understand before calling yourself a backend engineer:

1. HTTP
2. DNS
3. TCP
4. SQL
5. Indexes
6. Transactions
7. Caching
8. Queues
9. Load balancing
10. Replication
11. Idempotency
12. Rate limiting
13. Authentication
14. Observability
15. Distributed systems

Don’t just bookmark this.

Learn them.

[Umesh Kumar Yadav @Umesh__digital]

As a backend engineer.

Please learn below:

- System Design (scalability, microservices)

-APIs (REST, GraphQL, gRPC)

-Database Systems (SQL, NoSQL)

-Distributed Systems (consistency, replication)

-Caching (Redis, Memcached)

-Security (OAuth2, JWT, encryption)

-DevOps (CI/CD, Docker, Kubernetes)

-Performance Optimization (profiling, load balancing)

-Cloud Services (AWS, GCP, Azure)

-Monitoring (Prometheus, Grafana)

Stop jumping from one language to the other

[Puttaraju Rangaswamy @puttarajubr]

@NarayanaRaoCK1 @siddaramaiah ನಿಮ್ಮನೆ issues ನೀವು solve ಮಾಡಿಕೊಳ್ಳಿ,

He's national level leader his Job is to concentrate delhi,
So you are agreeing no hope on BJP !!

[Tips Excel @gudanglifehack]

🚀 AI Engineer Learning Roadmap (2026)

🎯 Step 1: Learn Python
✅ Variables & Data Types
✅ Loops & Functions
✅ Object-Oriented Programming
✅ Exception Handling
✅ File Handling
✅ Modules & Packages

🎯 Step 2: Master Python Libraries
✅ NumPy
✅ Pandas
✅ Matplotlib
✅ Scikit-learn

🎯 Step 3: Learn AI Fundamentals
✅ Artificial Intelligence
✅ Machine Learning
✅ Deep Learning
✅ Neural Networks
✅ Generative AI
✅ Large Language Models (LLMs)

🎯 Step 4: Learn Prompt Engineering
✅ Zero-shot Prompting
✅ One-shot Prompting
✅ Few-shot Prompting
✅ Role Prompting
✅ Chain of Thought Prompting

🎯 Step 5: Learn LLMs
✅ Tokenization
✅ Embeddings
✅ Context Window
✅ Temperature
✅ Model Parameters

🎯 Step 6: Learn RAG
✅ Embeddings
✅ Chunking
✅ Vector Databases
✅ Similarity Search
✅ Retrieval Pipeline

🎯 Step 7: Learn AI Agents
✅ Agent Architecture
✅ Memory
✅ Planning
✅ Tool Calling
✅ Multi-Agent Systems

🎯 Step 8: Learn AI Frameworks
✅ LangChain
✅ LangGraph
✅ LlamaIndex
✅ CrewAI

🎯 Step 9: Learn Backend Development
✅ FastAPI
✅ REST APIs
✅ Authentication
✅ Database Integration

🎯 Step 10: Deploy AI Applications
✅ Docker
✅ Git & GitHub
✅ Cloud Platforms (AWS, Azure, or GCP)
✅ Monitoring & Logging

🎯 Step 11: Build Projects
✅ AI Chatbot
✅ PDF Chat Assistant
✅ AI Research Assistant
✅ AI Resume Analyzer
✅ Customer Support Agent
✅ AI Coding Assistant

💡 Final Advice
The fastest way to become an AI Engineer isn't by learning every AI tool. Build projects that combine Python, LLMs, RAG, AI Agents, APIs, and deployment. That's the skill set companies are hiring for in 2026.

Double Tap ❤️ For More